1. Field of the Invention
The present general inventive concept relates to an image interpolation apparatus and method, and more particularly, to an image interpolation apparatus and a method of improving characteristics of an edge portion of an image signal.
2. Description of the Related Art
In general, when an image display device receives an image having a resolution that is different from a resolution that is preset in the image display device, the image display device is required to convert the resolution of the image to match the preset resolution of the image display device.
If the resolution of the image input to the image display device is different from the preset resolution of the image display device, the resolution of the image is converted by either increasing or decreasing a number of pixels of the input image to interpolate (i.e., upscale) or decimate (i.e., downscale) the resolution of the image, respectively. The conversion of the image is typically referred to as scaling or format conversion. In particular, if an image having a lower resolution than the preset resolution of the image display device is input thereto, the image display device uses a linear interpolation method to vertically and/or horizontally upscale the lower resolution of the image to match the preset resolution.
The linear interpolation method can include a bi-linear interpolation method, a cubic convolution interpolation method, and the like. FIGS. 1A and 1B are views illustrating the bi-linear interpolation method and the cubic convolution interpolation method respectively used by a conventional image interpolation apparatus. The bi-linear interpolation method and the cubic convolution interpolation method use a finite impulse response (FIR) filter to convert an input image signal into a frequency domain and then filter the frequency domain image signal using weights of pixels that neighbor an interpolation position (i.e., a pixel being interpolated). As a result, upscaled interpolation data is output. For example, in the bi-linear interpolation method, input image signals are interpolated using 2-tab filtering as illustrated in FIG. 1A. In other words, the bi-linear interpolation method is performed using two pixels that neighbor a position to be interpolated (i.e., a pixel).
In the cubic convolution interpolation method, input image signals are interpolated using 4-tab filtering as illustrated in FIG. 1B. In other words, the cubic convolution interpolation method is performed using four pixels that neighbor a position to be interpolated. However, the conventional image interpolation apparatus typically performs interpolation using only one interpolation method (i.e., one preset filtering). Thus, image quality in each frequency domain of an image signal may deteriorate. For example, when the conventional image interpolation apparatus interpolates an input image signal using the cubic convolution interpolation method, image quality may not deteriorate in portions of the image having high frequency components. However, when the conventional image interpolation apparatus interpolates the input image signal using the bi-linear interpolation method, image quality may deteriorate in portions of the image having the high frequency components.
Most image display devices typically use the cubic convolution interpolation method, a convolution type image interpolation method (e.g., a sinc interpolation method), or both the cubic convolution interpolation method and the convolution type image interpolation method. When the image display devices selectively adopt the cubic convolution interpolation method or the sinc interpolation method to reproduce an image, it can be difficult to reduce blurring occurring in an edge portion of the image.
When the cubic convolution interpolation method is used, a ringing phenomenon hardly occurs in an edge area of an image signal but a blurring phenomenon occurs in the edge area. When the sinc interpolation method is used, a frequency characteristic is good in a low frequency domain (i.e., a domain in which a change in the image is low) but ringing may occur in the edge area. FIG. 2 is a graph illustrating response characteristics of a square wave A according to the cubic convolution interpolation method. As illustrated in FIG. 2, the cubic convolution interpolation method is used with respect to the square wave A, blurring represented by a line graph B occurs in the edge portion. Thus, image quality of the edge portion deteriorates. When the sinc interpolation method is used with respect to the square wave A, ringing represented by a line graph C occurs in the edge area.
Accordingly, when the conventional image reproducing apparatus reproduces an image by selectively using the cubic convolution interpolation method or the sinc interpolation method, it becomes necessary to determine a method of reducing a blurring that occurs in the edge portion when the cubic convolution interpolation method is used.
When the cubic convolution interpolation method is used with respect to an image signal, blurring occurs in the edge portion of the image signal. However, an 8-tab poly phase interpolation method, which is a type of sinc interpolation method, can be used to reduce blurring in the edge portion. However, in this case, ringing occurs in the edge area.